Human and automated decisions are presumably made using information which may be relevant to the decisions, and/or to the outcomes of the decisions. Decision support thus generally refers to the field of obtaining and providing such information in a manner best-suited to assist in the decision-making. Many different fields and settings may benefit from such decision support, including, to name a few examples, the realms of business, legal, educational, governmental, health, military, and personal. In a business setting, for example, an equities manager may wish to make a decision about whether to purchase a particular equity, and may wish to have access to information which may assist in making such a decision.
In an ideal situation, decision makers may easily be presented with exactly the information needed to make the decision(s), e.g., all available information may be up-to-date, and may be parsed such that only desired/necessary information is extracted to be provided to the decision maker. In reality, it is difficult or impossible to reach such an ideal solution. For example, the necessary information may be large in amount, and/or may be distributed across a large geographical area (e.g., in multiple datacenters), perhaps stored in heterogeneous systems. Meanwhile, some information is time critical for some decisions, and therefore rapidly becomes out of date and useless for decision support. On the other hand, other information may remain current almost indefinitely for purposes of making the same or different decision(s). Considering these and other factors, then, it may be seen that it may be problematic to identify and obtain desired information in a time frame necessary to make an acceptable decision.